Synthetic-aperture radar processing using fast factorized back-projection

Exact synthetic aperture radar (SAR) inversion for a linear aperture may be obtained using fast transform techniques. Alternatively, back-projection integration in time domain can also be used. This technique has the benefit of handling a general aperture geometry. In the past, however, back-projection has seldom been used due to heavy computational burden. We show that the back-projection integral can be recursively partitioned and an effective algorithm constructed based on aperture factorization. By representing images in local polar coordinates it is shown that the number of operations is drastically reduced and can be made to approach that of fast transform algorithms. The algorithm is applied to data from the airborne ultra-wideband CARABAS SAR and shown to give a reduction in processing time of two to three orders of magnitude.     

Instability and unsteadiness of aircraft wake vortices

This paper presents a review of theoretical and experimental results on stability and other unsteady properties of aircraft wakes. The basic mechanisms responsible for the propagation and the amplification of perturbation along vortices, namely the Kelvin waves and the cooperative instabilities, are first detailed. These two generic unsteady mechanisms are described by considering asymptotic linear stability analysis of model flows such as vortex filaments or Lamb–Oseen vortices. Extension of the linear analysis to more representative flows, using a biglobal stability approach, is also described. Experimental results obtained using LDV, hot wire and PIV in wind tunnels are presented and they are commented upon the light of theory.     

Huygens Probe Aerosol Collector Pyrolyser Experiment

ACP's main objective is the chemical analysis of the aerosols in Titan's atmosphere. For this purpose, it will sample the aerosols during descent and prepare the collected matter (by evaporation, pyrolysis and gas products transfer) for analysis by the Huygens Gas Chromatograph Mass Spectrometer (GCMS). A sampling system is required for sampling the aerosols in the 135'32 km and 22'17 km altitude regions of Titan's atmosphere. A pump unit is used to force the gas flow through a filter. In its sampling position, the filter front face extends a few mm beyond the inlet tube. The oven is a pyrolysis furnace where a heating element can heat the filter and hence the sampled aerosols to 250 °C or 600 °C. The oven contains the filter, which has a thimble-like shape (height 28 mm). For transferring effluent gas and pyrolysis products to GCMS, the carrier gas is a labeled nitrogen ¹⁵N₂, to avoid unwanted secondary reactions with Titan's atmospheric nitrogen. Aeraulic tests under cold temperature conditions were conducted by using a cold gas test system developed by ONERA. The objective of the test was to demonstrate the functional ability of the instrument during the descent of the probe and to understand its thermal behavior, that is to test the performance of all its components, pump unit and mechanisms. In order to validate ACP's scientific performance, pyrolysis tests were conducted at LISA on solid phase material synthesized from experimental simulation. The chromatogram obtained by GCMS analysis shows many organic compounds. Some GC peaks appear clearly from the total mass spectra, with specific ions well identified thanks to the very high sensitivity of the mass spectrometer. The program selected for calibrating the flight model is directly linked to the GCMS calibration plan. In order not to pollute the two flight models with products of solid samples such as tholins, we excluded any direct pyrolysis tests through the ACP oven during the first phase of the calibration. Post probe descent simulation of flight results are planned, using the much representative GCMS and ACP spare models. This revised version was published online in August 2006 with corrections to the Cover Date.     

Detection of small objects in clutter using a GA-RBF neural network

Detection of small objects in a radar or satellite image is an important problem with many applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to reconstruct the chaotic dynamic of sea clutter. The reconstructed sea clutter dynamical system which usually takes the form of a nonlinear predictor does not only provide a model of the sea scattering phenomenon, but it can also be used to detect the existence of small targets such as fishing boats and small fragments of icebergs by observing abrupt changes in the prediction error. We applied a genetic algorithm (GA) to obtain an optimal reconstruction of sea clutter dynamic based on a radial basis function (RBF) neural network. This GA-RBF uses a hybrid approach that employes a GA to search for the optimum values of the following RBF parameters: centers, variance, and number of hidden nodes, and uses the least square method to determine the weights. It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net (i.e., no approximation error), this GA-RBF approach can reconstruct the exact dynamic from its time series measurements. In addition to the improved accuracy in modeling sea clutter dynamic, the GA-RBF is also shown to enhance the detectability of small objects embedded in the sea. Using real-life radar data that are collected in the east coast of Canada by two different radar systems: a ground-based radar and a satellite equipped with synthetic aperture radar (SAR), we show that the GA-RBF network is a reliable detector for small surface targets in various sea conditions and is practical for real-life search and rescue, navigation, and surveillance applications     

Nonstochastic adaptive decision fusion in distributed-detection systems

Implementing the optimal Neyman-Pearson (NP) fusion rule in distributed detection systems requires the sensor error probabilities to be a priori known and constant during the system operation. Such a requirement is practically impossible to fulfil for every resolution cell in a multiflying target multisensor environment. The true performance of the fusion center is often worse than expected due to fluctuations of the observed environment and instabilities of sensor thresholds. This work considers a nonparametric data fusion situation in which the fusion center knows only the number of the sensors, but ignores their error probabilities and cannot control their thresholds. A data adaptive approach to the problem is adopted, and combining P reports from Q independent distributed sensors through a least squares (LS) formulation to make a global decision is investigated. Such a fusion scheme does not entail strict stationarity of the noise environment nor strict invariance of the sensor error probabilities as is required in the NP formulation. The LS fusion scheme is analyzed in detail to simplify its form and determine its asymptotic behavior. Conditions of performance improvement as P increases and of quickness of such improvement are found. These conditions are usually valid in netted radar surveillance systems.     

Near Infrared Spectrometer for the Near Earth Asteroid Rendezvous Mission

The Near-Infrared Spectrometer (NIS) instrument on the Near-Earth Asteroid Rendezvous (NEAR) spacecraft is designed to map spectral properties of the mission target, the S-type asteroid 433 Eros, at near-infrared wavelengths diagnostic of the composition of minerals forming S asteroids. NIS is a grating spectrometer, in which light is directed by a dichroic beam-splitter onto a 32-element Ge detector (center wavelengths, 816–1486 nm) and a 32-element InGaAs detector (center wavelengths, 1371–2708 nm). Each detector reports a 32-channel spectrum at 12-bit quantization. The field-of-view is selectable using slits with dimensions calibrated at 0.37° × 0.76° (narrow slit) and 0.74° × 0.76° (wide slit). A shutter can be closed for dark current measurements. For the Ge detector, there is an option to command a 10x boost in gain. A scan mirror rotates the field-of-view over a 140° range, and a diffuse gold radiance calibration target is viewable at the sunward edge of the field of regard. Spectra are measured once per second, and up to 16 can be summed onboard. Hyperspectral image cubes are built up by a combination of down-track spacecraft motion and cross-track scanning of the mirror. Instrument software allows execution of data acquisition macros, which include selection of the slit width, number of spectra to sum, gain, mirror scanning, and an option to interleave dark spectra with the shutter closed among asteroid observations. The instrument was extensively characterized by on-ground calibration, and a comprehensive program of in-flight calibration was begun shortly after launch. NIS observations of Eros will largely be coordinated with multicolor imaging from the Multispectral Imager (MSI). NIS will begin observing Eros during approach to the asteroid, and the instrument will map Eros at successively higher spatial resolutions as NEAR's orbit around Eros is lowered incrementally to 25 km altitude. Ultimate products of the investigation will include composition maps of the entire illuminated surface of Eros at spatial resolutions as high as 300 m.     

Fuzzy logic applications to multisensor-multitarget correlation

A consistent tactical picture requires data fusion technology to combine and propagate information received from diverse objects and usually vague situations. The information may be contained in two types of data; numerical data received from sensor measurements, and linguistic data obtained from human operators and domain experts. In real world situations, the numerical data may be noisy, inconsistent, and incomplete, and the linguistic information is imprecise and vague. To deal with these two types of data simultaneously, fuzzy sets and fuzzy logic provide a methodology to obtain an approximate but consistent tactical picture in a timely manner for very complex or ill-defined engineering problems. A functional paradigm for fuzzy data fusion is presented. It consists of four basic elements: (1) fuzzification of crisp elements, (2) fuzzy knowledge base derived from numerical input/output relations and humans, (3) fuzzy inference mechanism based on a class of fuzzy logic, (4) defuzzification of fuzzy outputs into crisp outputs for use by a plant. For real-time practical systems, the on-line determination of a fuzzy membership function from a given set of crisp inputs is vital. To this end, a methodology for estimating an optimal membership function from crisp input data has been implemented. This is based on the possibility/probability consistency principle as proposed by L.A. Zadeh. A relationship between the fuzzy membership function and the confidence level of statistical input data has been developed and it serves as a design parameter for fuzzification. This technique has been applied to a two-dimensional multisensor-multitarget tracking system. Fuzzy system performance evaluations have been presented. With simulated data in the laboratory environment, the simulation has been performed to evaluate the Mission Avionics Sensor Synergism (MASS) Systems. These results show better performance for the data correlation function using the fuzzy logic techniques.     

Implications of X-ray Observations for Electron Acceleration and Propagation in Solar Flares

High-energy X-rays and ??-rays from solar flares were discovered just over fifty years ago. Since that time, the standard for the interpretation of spatially integrated flare X-ray spectra at energies above several tens of keV has been the collisional thick-target model. After the launch of the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) in early 2002, X-ray spectra and images have been of sufficient quality to allow a greater focus on the energetic electrons responsible for the X-ray emission, including their origin and their interactions with the flare plasma and magnetic field. The result has been new insights into the flaring process, as well as more quantitative models for both electron acceleration and propagation, and for the flare environment with which the electrons interact. In this article we review our current understanding of electron acceleration, energy loss, and propagation in flares. Implications of these new results for the collisional thick-target model, for general flare models, and for future flare studies are discussed.     

Expectations for Infrared Spectroscopy of 9P/Tempel 1 from Deep Impact

The science payload on the Deep Impact mission includes a 1.05–4.8 μm infrared spectrometer with a spectral resolution ranging from R∼200–900. The Deep Impact IR spectrometer was designed to optimize, within engineering and cost constraints, observations of the dust, gas, and nucleus of 9P/Tempel 1. The wavelength range includes absorption and emission features from ices, silicates, organics, and many gases that are known to be, or anticipated to be, present on comets. The expected data will provide measurements at previously unseen spatial resolution before, during, and after our cratering experiment at the comet 9P/Tempel 1. This article explores the unique aspects of the Deep Impact IR spectrometer experiment, presents a range of expectations for spectral data of 9P/Tempel 1, and summarizes the specific science objectives at each phase of the mission.